<?xml version="1.0"?>
<doc>
    <assembly>
        <name>OptimizationToolbox</name>
    </assembly>
    <members>
        <member name="T:OptimizationToolbox.abstractMeritFunction">
            <summary>
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractMeritFunction.optMethod">
            <summary>
            references back to the optimization method
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractMeritFunction.#ctor(OptimizationToolbox.abstractOptMethod,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractMeritFunction"/> class.
            </summary>
            <param name="optMethod">The opt method.</param>
            <param name="penaltyWeight">The penalty weight.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractMeritFunction.calcGradientOfPenalty(System.Double[])">
            <summary>
            Calcs the gradient of penalty.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractMeritFunction.calcPenalty(System.Double[])">
            <summary>
            Calcs the penalty.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="P:OptimizationToolbox.abstractMeritFunction.penaltyWeight">
            <summary>
            Gets or sets the penalty weight.
            </summary>
            <value>The penalty weight.</value>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.CreateNeighborChangeVectors(System.Int32)">
            <summary>
              Creates the neighbor change vectors. There will at least the minimum specified, 
              and the process will stop after this max is reached although there may be significantly
              more which are created to keep the changes symmetric. This is probably one of the
              craziest little functions I've ever written but there is a method to it madness.
              As opposed to the simplest approach which is +/-1 step in each direction, it seems
              beneficial to have more transitions that can be made. And as opposed to increasing
              these sizes linearly (e.g. +/-1, +/-2, +/-3, ...), it seems better to have them
              increase logarithmically. Here the steps are 1,3,7,20,55,etc. The idea is to move
              in the closest integers to the natural log. As if in base-e. This is shown to
              be optimal from a simple paper I read in science some years ago:
              http://www.americanscientist.org/issues/pub/third-base/3
              The function starts at points e^0 (or 1) away and makes the primary changes,
              {(-1,0), (+1,0), (0,-1), (0,+1)}, and then goes on to e^1 rounded to the closest
              integer {(-3,0), (+3,0), (0,-3), (0,+3)}. But then it goes back to fill out the 
              higher order changes at the lower levels {(-1,-1), (+1,-1), (-1,+1), (+1,+1)}.
              It then jumps to the next exponent for a new set of primary changes, and then
              again drops back to populate the higher level changes of lower levels.
            </summary>
            <param name = "minimumNeighbors">The minimum neighbors.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.GetEnumerator">
            <summary>
            Returns an enumerator that iterates through the collection.
            </summary>
            <returns>
            A <see cref="T:System.Collections.Generic.IEnumerator`1"/> that can be used to iterate through the collection.
            </returns>
            <filterpriority>1</filterpriority>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.System#Collections#IEnumerable#GetEnumerator">
            <summary>
            Returns an enumerator that iterates through a collection.
            </summary>
            <returns>
            An <see cref="T:System.Collections.IEnumerator"/> object that can be used to iterate through the collection.
            </returns>
            <filterpriority>2</filterpriority>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.Add(OptimizationToolbox.VariableDescriptor)">
            <summary>
            Adds an item to the <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </summary>
            <param name="item">The object to add to the <see cref="T:System.Collections.Generic.ICollection`1"/>.
                            </param><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.ICollection`1"/> is read-only.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.Clear">
            <summary>
            Removes all items from the <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </summary>
            <exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.ICollection`1"/> is read-only. 
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.Contains(OptimizationToolbox.VariableDescriptor)">
            <summary>
            Determines whether the <see cref="T:System.Collections.Generic.ICollection`1"/> contains a specific value.
            </summary>
            <returns>
            true if <paramref name="item"/> is found in the <see cref="T:System.Collections.Generic.ICollection`1"/>; otherwise, false.
            </returns>
            <param name="item">The object to locate in the <see cref="T:System.Collections.Generic.ICollection`1"/>.
                            </param>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.CopyTo(OptimizationToolbox.VariableDescriptor[],System.Int32)">
            <summary>
            Copies the elements of the <see cref="T:System.Collections.Generic.ICollection`1"/> to an <see cref="T:System.Array"/>, starting at a particular <see cref="T:System.Array"/> index.
            </summary>
            <param name="array">The one-dimensional <see cref="T:System.Array"/> that is the destination of the elements copied from <see cref="T:System.Collections.Generic.ICollection`1"/>. The <see cref="T:System.Array"/> must have zero-based indexing.</param>
            <param name="arrayIndex">The zero-based index in <paramref name="array"/> at which copying begins.</param>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.Remove(OptimizationToolbox.VariableDescriptor)">
            <summary>
            Removes the first occurrence of a specific object from the <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </summary>
            <returns>
            true if <paramref name="item"/> was successfully removed from the <see cref="T:System.Collections.Generic.ICollection`1"/>; otherwise, false. This method also returns false if <paramref name="item"/> is not found in the original <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </returns>
            <param name="item">The object to remove from the <see cref="T:System.Collections.Generic.ICollection`1"/>.
                            </param><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.ICollection`1"/> is read-only.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.IndexOf(OptimizationToolbox.VariableDescriptor)">
            <summary>
            Determines the index of a specific item in the <see cref="T:System.Collections.Generic.IList`1"/>.
            </summary>
            <returns>
            The index of <paramref name="item"/> if found in the list; otherwise, -1.
            </returns>
            <param name="item">The object to locate in the <see cref="T:System.Collections.Generic.IList`1"/>.
                            </param>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.Insert(System.Int32,OptimizationToolbox.VariableDescriptor)">
            <summary>
            Inserts an item to the <see cref="T:System.Collections.Generic.IList`1"/> at the specified index.
            </summary>
            <param name="index">The zero-based index at which <paramref name="item"/> should be inserted.
                            </param><param name="item">The object to insert into the <see cref="T:System.Collections.Generic.IList`1"/>.
                            </param><exception cref="T:System.ArgumentOutOfRangeException"><paramref name="index"/> is not a valid index in the <see cref="T:System.Collections.Generic.IList`1"/>.
                            </exception><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.IList`1"/> is read-only.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.DesignSpaceDescription.RemoveAt(System.Int32)">
            <summary>
            Removes the <see cref="T:System.Collections.Generic.IList`1"/> item at the specified index.
            </summary>
            <param name="index">The zero-based index of the item to remove.
                            </param><exception cref="T:System.ArgumentOutOfRangeException"><paramref name="index"/> is not a valid index in the <see cref="T:System.Collections.Generic.IList`1"/>.
                            </exception><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.IList`1"/> is read-only.
                            </exception>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.AllDiscrete">
            <summary>
              Gets a value indicating whether [all discrete].
            </summary>
            <value><c>true</c> if [all discrete]; otherwise, <c>false</c>.</value>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.DiscreteVarIndices">
            <summary>
              Gets the discrete var indices.
            </summary>
            <value>The discrete var indices.</value>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.SizeOfSpace">
            <summary>
              Gets the size of space.
            </summary>
            <value>The size of space.</value>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.MaxVariableSizes">
            <summary>
              Gets the max variable sizes.
            </summary>
            <value>The max variable sizes.</value>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.Count">
            <summary>
            Gets the number of elements contained in the <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </summary>
            <returns>
            The number of elements contained in the <see cref="T:System.Collections.Generic.ICollection`1"/>.
            </returns>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.n">
            <summary>
            Gets the number of dimensions in the space, it is the same as DesignSpaceDescription.Count.
            </summary>
            <value>The number of dimensions, the number of variable descriptors.</value>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.IsReadOnly">
            <summary>
            Gets a value indicating whether the <see cref="T:System.Collections.Generic.ICollection`1"/> is read-only.
            </summary>
            <returns>
            true if the <see cref="T:System.Collections.Generic.ICollection`1"/> is read-only; otherwise, false.
            </returns>
        </member>
        <member name="P:OptimizationToolbox.DesignSpaceDescription.Item(System.Int32)">
            <summary>
            Gets or sets the element at the specified index.
            </summary>
            <returns>
            The element at the specified index.
            </returns>
            <param name="index">The zero-based index of the element to get or set.
                            </param><exception cref="T:System.ArgumentOutOfRangeException"><paramref name="index"/> is not a valid index in the <see cref="T:System.Collections.Generic.IList`1"/>.
                            </exception><exception cref="T:System.NotSupportedException">The property is set and the <see cref="T:System.Collections.Generic.IList`1"/> is read-only.
                            </exception>
        </member>
        <member name="T:OptimizationToolbox.abstractSelector">
            <summary>
            The class that all selector classes must inherit from. 
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractSelector.direction">
            <summary>
            the direction of the search: maximizing or minimizing
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractSelector.#ctor(OptimizationToolbox.optimize)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractSelector"/> class.
            </summary>
            <param name="direction">The direction.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractSelector.selectCandidates(System.Collections.Generic.List{OptimizationToolbox.Candidate}@,System.Double)">
            <summary>
            Selects the candidates.
            </summary>
            <param name="candidates">The candidates.</param>
            <param name="control">The control.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractSelector.betterThan(System.Double,System.Double)">
            <summary>
            if x betters the than y given the direction of the search
            maximizing or minimizing.
            </summary>
            <param name="x">The x.</param>
            <param name="y">The y.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.RandomPairwiseCompare.randomizeList(System.Collections.Generic.List{OptimizationToolbox.Candidate}@)">
            <summary>
            Randomizes the list.
            </summary>
            <param name="candidates">The candidates.</param>
        </member>
        <member name="T:OptimizationToolbox.abstractOptMethod">
            <summary>
            The main class that all optimization methods inherit from.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.ResetFunctionEvaluationDatabase">
            <summary>
            Resets the function evaluation database.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_f(System.Double[],System.Boolean)">
            <summary>
            Calculates the value of f at the specified point (assuming single-objective).
            </summary>
            <param name="point">The point.</param>
            <param name="includeMeritPenalty">if set to <c>true</c> [include merit penalty].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_f_vector(System.Double[],System.Boolean)">
            <summary>
            Calculates the f vector (multi-objective) at the specified point.
            </summary>
            <param name="point">The point.</param>
            <param name="includeMeritPenalty">if set to <c>true</c> [include merit penalty].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_h_vector(System.Double[])">
            <summary>
            Calculates the h vector at the specified point.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_g_vector(System.Double[])">
            <summary>
            Calculates the g vector at the specified point.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_active_vector(System.Double[])">
            <summary>
            Calculates the active vector at the specified point.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_f_gradient(System.Double[],System.Boolean)">
            <summary>
            Calculates the gradient of f vector at the specified point.
            </summary>
            <param name="point">The point.</param>
            <param name="includeMeritPenalty">if set to <c>true</c> [include merit penalty].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_h_gradient(System.Double[])">
             <summary>
            Calculates the gradient of h vector at the specified point.
             </summary>
             <param name="point">The point.</param>
             <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_g_gradient(System.Double[])">
            <summary>
            Calculates the gradient of g vector at the specified point.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_active_gradient(System.Double[])">
            <summary>
            Calculates the gradient of active vector at  the specified point.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_h_gradient(System.Double[],System.Collections.Generic.List{System.Int32})">
            <summary>
            Calculates the gradient of h vector at the specified point
            at the specified indices.
            </summary>
            <param name="point">The point.</param>
            <param name="Indices">The indices.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_g_gradient(System.Double[],System.Collections.Generic.List{System.Int32})">
            <summary>
            Calculates the gradient of g vector at the specified point
            at the specified indices.
            </summary>
            <param name="point">The point.</param>
            <param name="Indices">The indices.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.calc_active_gradient(System.Double[],System.Collections.Generic.List{System.Int32})">
            <summary>
            Calculates the gradient of active vector at the specified point
            at the specified indices.
            </summary>
            <param name="point">The point.</param>
            <param name="Indices">The indices.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.feasible(System.Double[])">
            <summary>
            Determines if the specified point is feasible.
            </summary>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.feasible(OptimizationToolbox.IInequality,System.Double[])">
            <summary>
            Determines if the specified point is feasible
            for the inequality, c.
            </summary>
            <param name="c">The c.</param>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.feasible(OptimizationToolbox.IEquality,System.Double[])">
            <summary>
            Determines if the specified point is feasible
            for the equality, c.
            </summary>
            <param name="c">The c.</param>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.feasible(OptimizationToolbox.IConstraint,System.Double[])">
            <summary>
            Determines if the specified point is feasible
            for the constraint, c.
            </summary>
            <param name="c">The c.</param>
            <param name="point">The point.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.deriv_wrt_xi(OptimizationToolbox.IOptFunction,System.Double[],System.Int32)">
            <summary>
            Calculates the derivative with respect to variable xi
            for the specified function.
            </summary>
            <param name="function">The function.</param>
            <param name="point">The point.</param>
            <param name="i">The index of the variable in x.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractOptMethod"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.Add(System.Object)">
            <summary>
            Adds the specified object to the optimization routine.
            </summary>
            <param name="function">The object, function.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.Run(System.Double[]@)">
            <summary>
            Runs the optimization process and returns the optimal as xStar 
            and the value of fStar is return by the function.
            </summary>
            <param name="xStar">The optimizer, xStar.</param>
            <returns>optimal value, fStar</returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.Run(System.Double[]@,System.Double[])">
            <summary>
            Runs the optimization process from the specified xInit and
            returns the optimal as xStar and the value of fStar is return by the function.
            </summary>
            <param name="xStar">The optimizer, xStar.</param>
            <param name="xInit">The initial or start point, xInit.</param>
            <returns>optimal value, fStar</returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.Run(System.Double[]@,System.Int32)">
            <summary>
            Runs the optimization process with the specified number of variables and
            returns the optimal as xStar and the value of fStar is return by the function.
            </summary>
            <param name="xStar">The optimizer, xStar.</param>
            <param name="NumberOfVariables">The number of variables.</param>
            <returns>optimal value, fStar</returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.run(System.Double[]@)">
            <summary>
            Runs the specified optimization method. This includes the details
            of the optimization method.
            </summary>
            <param name="xStar">The x star.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.createProblemDefinition">
            <summary>
            Creates a problem definition object from the details loaded in the
            optimization routine. For use in saving the data to XML, etc.
            </summary>
            <returns>the problem definition</returns>
        </member>
        <member name="M:OptimizationToolbox.abstractOptMethod.notConverged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Returns true is the process has not converged.
            </summary>
            <param name="iteration">The iteration.</param>
            <param name="numFnEvals">The num fn evals.</param>
            <param name="fBest">The f best.</param>
            <param name="xBest">The x best.</param>
            <param name="population">The population.</param>
            <param name="gradF">The grad F.</param>
            <returns></returns>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.m">
            <summary>
            Gets or sets the number of active constraints.
            </summary>
            <value>The number of active constraints, m.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.p">
            <summary>
            Gets or sets the number of equality constraints.
            </summary>
            <value>The number of equality constraints, p.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.q">
            <summary>
            Gets or sets the number of inequality constraints.
            </summary>
            <value>The number of inequality constraints, q.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.numEvals">
            <summary>
            Gets the number of function evaluations. This is actually the max of
            all functions (objective functions, equalities and inequalities) from
            the optimization run.
            </summary>
            <value>The num evals.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.n">
            <summary>
            Gets or sets the number of decision variables.
            </summary>
            <value>The number of decision variables, n.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.k">
            <summary>
            Gets the iteration count (can be set only by an optimization method).
            </summary>
            <value>The iteration count, k.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.fStar">
            <summary>
            Gets the value of the optimum for single objective problems (can be set only by an optimization method).
            </summary>
            <value>The value of the optimum, fstar.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.searchDirMethod">
            <summary>
            Gets the search dir method.
            Such objects must inherit from the abstractSearchDirection class.
            </summary>
            <value>The search dir method.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.lineSearchMethod">
            <summary>
            Gets the line search method.
            Such objects must inherit from the abstractLineSearch class.
            </summary>
            <value>The line search method.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.ConvergenceMethods">
            <summary>
            Gets the list of convergence methods.
            All objects in list inherit from the abstractConvergence class.
            </summary>
            <value>The convergence methods.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.spaceDescriptor">
            <summary>
            Gets the space descriptor.
            </summary>
            <value>The space descriptor.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresObjectiveFunction">
            <summary>
            Gets or sets a value indicating whether [requires objective function].
            </summary>
            <value>
            	<c>true</c> if [requires objective function]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.ConstraintsSolvedWithPenalties">
            <summary>
            Gets or sets a value indicating whether [constraints solved with penalties].
            </summary>
            <value>
            	<c>true</c> if [constraints solved with penalties]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresMeritFunction">
            <summary>
            Gets or sets a value indicating whether [requires merit function].
            
            </summary>
            <value>
            	<c>true</c> if [requires merit function]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.InequalitiesConvertedToEqualities">
            <summary>
            Gets or sets a value indicating whether [inequalities converted to equalities].
            </summary>
            <value>
            	<c>true</c> if [inequalities converted to equalities]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresSearchDirectionMethod">
            <summary>
            Gets or sets a value indicating whether [requires search direction method].
            </summary>
            <value>
            	<c>true</c> if [requires search direction method]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresLineSearchMethod">
            <summary>
            Gets or sets a value indicating whether [requires line search method].
            </summary>
            <value>
            	<c>true</c> if [requires line search method]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresAnInitialPoint">
            <summary>
            Gets or sets a value indicating whether [requires an initial point].
            </summary>
            <value>
            	<c>true</c> if [requires an initial point]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresConvergenceCriteria">
            <summary>
            Gets or sets a value indicating whether [requires convergence criteria].
            </summary>
            <value>
            	<c>true</c> if [requires convergence criteria]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresFeasibleStartPoint">
            <summary>
            Gets or sets a value indicating whether [requires feasible start point].
            </summary>
            <value>
            	<c>true</c> if [requires feasible start point]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.RequiresDiscreteSpaceDescriptor">
            <summary>
            Gets or sets a value indicating whether [requires discrete space descriptor].
            </summary>
            <value>
            	<c>true</c> if [requires discrete space descriptor]; otherwise, <c>false</c>.
            </value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.meritFunction">
            <summary>
            Gets the merit/penalty function method used by the optimization.
            This object inherits from the abstractMeritFunction class.
            </summary>
            <value>The merit function.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.xStart">
            <summary>
            Gets the initial or start x candidate.
            </summary>
            <value>The x start.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.x">
            <summary>
            Gets the running value of x.
            </summary>
            <value>The x.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.feasibleOuterLoopMax">
            <summary>
            Gets or sets the feasible outer loop max.
            </summary>
            <value>The feasible outer loop max.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.feasibleInnerLoopMax">
            <summary>
            Gets or sets the feasible inner loop max.
            </summary>
            <value>The feasible inner loop max.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.NumConvergeCriteriaNeeded">
            <summary>
            Gets or sets the num convergence criteria needed to stop the process.
            </summary>
            <value>The num converge criteria needed.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.ConvergenceDeclaredBy">
            <summary>
            Gets the criteria that declared convergence.
            </summary>
            <value>The convergence declared by.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractOptMethod.ConvergenceDeclaredByTypeString">
            <summary>
            Gets the convergence methods as a single (CSV) string of types.
            </summary>
            <value>The convergence declared by type string.</value>
        </member>
        <member name="T:OptimizationToolbox.abstractLineSearch">
            <summary>
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractLineSearch.epsilon">
            <summary>
            the tolerance value, epsilon is used to distinguish values of alpha. It is part
            of the convergence for the line search.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractLineSearch.k">
            <summary>
            the iterations are counted with k
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractLineSearch.kMax">
            <summary>
            Kmax is the maximum iterations to convergence.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractLineSearch.stepSize">
            <summary>
            stepSize is the discretization step taken between values of alpha
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.#ctor(System.Double,System.Double,System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractLineSearch"/> class.
            </summary>
            <param name="epsilon">The epsilon.</param>
            <param name="stepSize">Size of the step.</param>
            <param name="kMax">The k max.</param>
            <param name="trackFeasibility">if set to <c>true</c> [track feasibility].</param>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.findAlphaStar(System.Double[],System.Double[])">
            <summary>
            Finds the alpha star.
            </summary>
            <param name="x">The x.</param>
            <param name="dir">The dir.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.findAlphaStar(System.Double[],System.Double[],System.Boolean)">
            <summary>
            Finds the alpha star.
            </summary>
            <param name="x">The x.</param>
            <param name="dir">The dir.</param>
            <param name="allowNegAlpha">if set to <c>true</c> [allow neg alpha].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.findAlphaStar(System.Double[],System.Double[],System.Double)">
            <summary>
            Finds the alpha star.
            </summary>
            <param name="x">The x.</param>
            <param name="dir">The dir.</param>
            <param name="initAlpha">The init alpha.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.findAlphaStar(System.Double[],System.Double[],System.Boolean,System.Double)">
            <summary>
            Finds the alpha star.
            </summary>
            <param name="x">The x.</param>
            <param name="dir">The dir.</param>
            <param name="allowNegAlpha">if set to <c>true</c> [allow neg alpha].</param>
            <param name="initAlpha">The init alpha.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractLineSearch.calcF(System.Double[],System.Double,System.Double[])">
            <summary>
            Calcs the objective function value.
            </summary>
            <param name="start">The start.</param>
            <param name="alpha">The alpha.</param>
            <param name="dir">The dir.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.FixedOrGoldenSection.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.GoldenSection"/> class.
            Unlike other line search methods, there is no need to provide a kmax.
            This is determined directly from the epsilon in the body of the code
            </summary>
            <param name="epsilon">The epsilon.</param>
            <param name="upperBound">The upper bound.</param>
        </member>
        <member name="T:OptimizationToolbox.abstractConvergence">
            <summary>
            The abstract class that all convergence criteria must inherit from. There is one Boolean function
            that is returned from the class, called "converged", which takes up to five arguments.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Has the optimization algorithm converged? Each criteria that overrides this is OR'ed together
            that means only one critieria needs to return true.
            </summary>
            <param name="iteration">The number of iterations.</param>
            <param name="numFnEvals">The number of function evaluations.</param>
            <param name="fBest">The best f.</param>
            <param name="xBest">The best x.</param>
            <param name="population">The population of candidates.</param>
            <param name="gradF">The gradient of F.</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="M:OptimizationToolbox.DeltaGradFConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.DeltaGradFConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.DeltaGradFConvergence.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.DeltaGradFConvergence"/> class.
            </summary>
            <param name="minDifference">The min difference.</param>
        </member>
        <member name="M:OptimizationToolbox.DeltaGradFConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value D (minimum difference), this criteria will return true, if the norm 
            (1-norm sum of absolute values) of the gradient less than or equal to D.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x (not used).</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F.</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.DeltaGradFConvergence.minDifference">
            <summary>
            Gets or sets the min difference.
            </summary>
            <value>The min difference.</value>
        </member>
        <member name="M:OptimizationToolbox.Elitism.sort(System.Collections.Generic.List{OptimizationToolbox.Candidate}@)">
            <summary>
            Sorts the specified candidates.
            </summary>
            <param name="candidates">The candidates.</param>
        </member>
        <member name="T:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule">
            <summary>
            the class that all simulated annealing cooling schedules must inherit from.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule.samplesInGeneration">
            <summary>
            the number of samples to take in determining the temperature.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule.optMethod">
            <summary>
            the reference back to the entire simulated annealing optimization method.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule.samplesThusFar">
            <summary>
            number of samples taken thus far
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule.#ctor(System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule"/> class.
            </summary>
            <param name="samplesInGeneration">The samples in generation.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractSimulatedAnnealingCoolingSchedule.SetOptimizationDetails(OptimizationToolbox.abstractOptMethod)">
            <summary>
            Sets the optimization details.
            </summary>
            <param name="optMethod">The opt method.</param>
        </member>
        <member name="T:OptimizationToolbox.SamplingGenerator">
            <summary>
            The Sampling Generator abstract class is used to indicate which generators are used for initial creation of points.
            These could also be used for simple design space exploration as in design of experiments. One may consider writing
            additional types like OFAT (one factor at a time), Full-Factorial, Fractional-Factorial, Box-Bencken, etc.
            </summary>
        </member>
        <member name="T:OptimizationToolbox.abstractGenerator">
            <summary>
            The abstract generator class is used for all discrete problems. The generator
            creates new solutions. Either by adding to the input list or by simply writing over
            it.
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractGenerator.discreteSpaceDescriptor">
            <summary>
            The discreteSpaceDescriptor is of type DesignSpaceDescription and includes the
            details (VariableDescriptors) for all variables in the system.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractGenerator.#ctor(OptimizationToolbox.DesignSpaceDescription)">
            <summary>
              Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractGenerator"/> class.
            </summary>
            <param name="discreteSpaceDescriptor">The discrete space descriptor.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractGenerator.GenerateCandidates(System.Collections.Generic.List{OptimizationToolbox.Candidate}@,System.Int32)">
            <summary>
            Generates the candidates.
            </summary>
            <param name="candidates">The candidates.</param>
            <param name="control">The control.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractGenerator.GenerateCandidates(System.Double[],System.Int32)">
            <summary>
            Generates the candidates.
            </summary>
            <param name="candidate">The candidate.</param>
            <param name="control">The control.</param>
            <returns></returns>
        </member>
        <member name="P:OptimizationToolbox.abstractGenerator.n">
            <summary>
            Gets the number of dimensions (length of the decision vector, x).
            </summary>
            <value>The n.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractGenerator.DiscreteVarIndices">
            <summary>
            Gets the indices for the discrete variables in x.
            </summary>
            <value>The discrete var indices.</value>
        </member>
        <member name="P:OptimizationToolbox.abstractGenerator.MaxVariableSizes">
            <summary>
            Gets the maximum variable sizes.
            </summary>
            <value>The max variable sizes.</value>
        </member>
        <member name="M:OptimizationToolbox.SamplingGenerator.#ctor(OptimizationToolbox.DesignSpaceDescription)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.SamplingGenerator"/> class.
            </summary>
            <param name="discreteSpaceDescriptor">The discrete space descriptor.</param>
        </member>
        <member name="T:OptimizationToolbox.abstractSearchDirection">
            <summary>
            The class that all search direction methods must inherit from. By search direction, we mean
            the vector, d that the numerical method must search in. The simplest example being SteepestDescent - 
            in the opposite direction of the gradient.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractSearchDirection.find(System.Double[],System.Double[],System.Double,System.Double@,System.Boolean)">
            <summary>
            Finds the direction for the specified x.
            </summary>
            <param name="x">The x.</param>
            <param name="gradf">The gradf.</param>
            <param name="f">The f.</param>
            <param name="initAlpha">The init alpha.</param>
            <param name="reset">if set to <c>true</c> [reset].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.abstractSearchDirection.find(System.Double[],System.Double[],System.Double,System.Boolean)">
            <summary>
            Finds the direction for the specified x.
            </summary>
            <param name="x">The x.</param>
            <param name="gradf">The gradf.</param>
            <param name="f">The f.</param>
            <param name="reset">if set to <c>true</c> [reset].</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.DeltaXConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.DeltaXConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.DeltaXConvergence.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.DeltaXConvergence"/> class.
            </summary>
            <param name="minDifference">The min difference.</param>
        </member>
        <member name="M:OptimizationToolbox.DeltaXConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value D (minimum difference), this criteria will return true, if the norm of 
            the difference between xBest and xlast is less than or equal to D.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x.</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.DeltaXConvergence.minDifference">
            <summary>
            Gets or sets the min difference, D.
            </summary>
            <value>The min difference.</value>
        </member>
        <member name="T:OptimizationToolbox.GeneticCrossoverGenerator">
            <summary>
            The crossover abstract class is simply used by the genetic algorithm to recognize which generators are to be
            used for crossover (as opposed to for initial sampling or mutation).
            </summary>
        </member>
        <member name="M:OptimizationToolbox.GeneticCrossoverGenerator.#ctor(OptimizationToolbox.DesignSpaceDescription)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.GeneticCrossoverGenerator"/> class.
            </summary>
            <param name="discreteSpaceDescriptor">The discrete space descriptor.</param>
        </member>
        <member name="T:OptimizationToolbox.GeneticMutationGenerator">
            <summary>
            The mutation abstract class is simply used by the genetic algorithm to recognize which generators are to be
            used for mutation (as opposed to for initial sampling or crossover). 
            </summary>
        </member>
        <member name="M:OptimizationToolbox.GeneticMutationGenerator.#ctor(OptimizationToolbox.DesignSpaceDescription)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.GeneticMutationGenerator"/> class.
            </summary>
            <param name="discreteSpaceDescriptor">The discrete space descriptor.</param>
        </member>
        <member name="T:OptimizationToolbox.abstractMOSelector">
            <summary>
            The class that all selector classes must inherit from. 
            </summary>
        </member>
        <member name="F:OptimizationToolbox.abstractMOSelector.optDirections">
            <summary>
            the direction of the search: maximizing or minimizing
            </summary>
        </member>
        <member name="M:OptimizationToolbox.abstractMOSelector.#ctor(System.Int32,OptimizationToolbox.optimize[])">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.abstractSelector"/> class.
            </summary>
            <param name="direction">The direction.</param>
        </member>
        <member name="M:OptimizationToolbox.abstractMOSelector.selectCandidates(System.Collections.Generic.List{OptimizationToolbox.Candidate}@,System.Double)">
            <summary>
            Selects the candidates.
            </summary>
            <param name="candidates">The candidates.</param>
            <param name="control">The control.</param>
        </member>
        <member name="M:OptimizationToolbox.WeightedCuboidParetoSelector.dominatesWithWeights(OptimizationToolbox.Candidate,OptimizationToolbox.Candidate)">
            <summary>
            Does c1 dominate c2?
            </summary>
            <param name="c1">the subject candidate, c1 (does this dominate...).</param>
            <param name="c2">the object candidate, c2 (is dominated by).</param>
            <returns></returns>
        </member>
        <member name="T:OptimizationToolbox.ArithmeticMean">
            <summary>
            Arithmetic Mean 1-D search as described by ? Rao(?)
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ArithmeticMean.#ctor(System.Double,System.Double,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ArithmeticMean"/> class.
            </summary>
            <param name="epsilon">The epsilon.</param>
            <param name="stepSize">Size of the step.</param>
            <param name="kMax">The k max.</param>
        </member>
        <member name="M:OptimizationToolbox.ArithmeticMean.findAlphaStar(System.Double[],System.Double[])">
            <summary>
            Finds the alpha star.
            </summary>
            <param name="x">The x.</param>
            <param name="dir">The dir.</param>
            <returns></returns>
        </member>
        <member name="T:OptimizationToolbox.Candidate">
            <summary>
            
            </summary>
        </member>
        <member name="M:OptimizationToolbox.Candidate.#ctor(System.Double[],OptimizationToolbox.abstractOptMethod)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.Candidate"/> class.
            </summary>
            <param name="x">The x.</param>
            <param name="evaluationMethods">the evaluation methods </param>
        </member>
        <member name="P:OptimizationToolbox.Candidate.fValues">
            <summary>
            Gets or sets the f values.
            </summary>
            <value>
            The f values.
            </value>
        </member>
        <member name="P:OptimizationToolbox.Candidate.gValues">
            <summary>
            Gets or sets the g values.
            </summary>
            <value>
            The g values.
            </value>
        </member>
        <member name="P:OptimizationToolbox.Candidate.hValues">
            <summary>
            Gets or sets the h values.
            </summary>
            <value>
            The h values.
            </value>
        </member>
        <member name="P:OptimizationToolbox.Candidate.x">
            <summary>
            Gets or sets the x vector - the vector of design variables.
            </summary>
            <value>
            The x.
            </value>
        </member>
        <member name="T:OptimizationToolbox.MaxFnEvalsConvergence">
            <summary>
            Given a value Kmax, this criteria will return true if the process reaches this many iterations.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxFnEvalsConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxIterationsConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxFnEvalsConvergence.#ctor(System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxIterationsConvergence"/> class.
            </summary>
            <param name="maxFunctionEvaluations">The max function evaluations.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxFnEvalsConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value Kmax, this criteria will return true if the process reaches this many function evaluations.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x (not used).</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.MaxFnEvalsConvergence.maxFunctionEvaluations">
            <summary>
            Gets or sets the maximum number of function evaluations.
            </summary>
            <value>The max function evaluations.</value>
        </member>
        <member name="M:OptimizationToolbox.SearchIO.output(System.Object,System.Int32)">
            <summary>
             Calling SearchIO.output will output the string, message, to the 
             text display on the right of GraphSynth, but ONLY if the verbosity (see
             below) is greater than or equal to your specified limit for this message.
             the verbosity limit must be 0, 1, 2, 3, or 4.
            </summary>
            <param name="message">The message.</param>
            <param name="verbosityLimit">The verbosity limit.</param>
        </member>
        <member name="M:OptimizationToolbox.SearchIO.output(System.Object[])">
            <summary>
            Outputs the one item of the specified list corresponding to the particular verbosity.
            </summary>
            <param name="list">The list.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.DeltaFConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value D (minimum difference), this criteria will return true, if the distance (absolute value of the difference) 
            between fBest and flast is less than or equal to D.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f.</param>
            <param name="xBest">The best x (used to check if candidate is the same).</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.DeltaFConvergence.minDifference">
            <summary>
            Gets or sets the minimum difference.
            </summary>
            <value>The min difference.</value>
        </member>
        <member name="P:OptimizationToolbox.DeltaFConvergence.toleranceForSame">
            <summary>
            Gets or sets the tolerance for same x. If the x is the same as the last the condition is NOT checked (returns false).
            If this is not a desirable catch, then leave as the default (negative infinity). In fact any negative value for
            toleranceForSame will cause it to be ignored since the distance between any two points can at best be 0.
            </summary>
            <value>The tolerance for same.</value>
        </member>
        <member name="T:OptimizationToolbox.ImplicitEnueration">
            <summary>
            Implicit Enumeration is a catch-all for Composite Decision Process (as
            deemed by Kanal and Kumar, 1988) for methods like Branch-and-Bound, 
            best first search, and A*.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ImplicitEnueration.#ctor(OptimizationToolbox.DesignSpaceDescription,OptimizationToolbox.optimize)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ImplicitEnueration"/> class.
            </summary>
            <param name="SpaceDescription">The space description.</param>
            <param name="direction">The direction.</param>
        </member>
        <member name="P:OptimizationToolbox.ImplicitEnueration.neighborGenerator">
            <summary>
            Gets the neighbor generator method class.
            </summary>
            <value>The neighbor generator.</value>
        </member>
        <member name="M:OptimizationToolbox.sameCandidate.Equals(System.Double[],System.Double[])">
            <summary>
            Determines whether the specified objects are equal.
            </summary>
            <param name="x">The first candidate x array to compare.</param>
            <param name="y">The second candidate x array to compare.</param>
            <returns>
            true if the specified objects are equal; otherwise, false.
            </returns>
        </member>
        <member name="M:OptimizationToolbox.sameCandidate.GetHashCode(System.Double[])">
            <summary>
            Returns a hash code for the specified object.
            </summary>
            <returns>
            A hash code for the specified object.
            </returns>
            <param name="obj">The <see cref="T:System.Object"/> for which a hash code is to be returned.
                            </param><exception cref="T:System.ArgumentNullException">The type of <paramref name="obj"/> is a reference type and <paramref name="obj"/> is null.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestXConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ToKnownBestXConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestXConvergence.#ctor(System.Double[],System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ToKnownBestXConvergence"/> class.
            </summary>
            <param name="xBest">The x best.</param>
            <param name="minDifference">The min difference.</param>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestXConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value for xAtOptimal and a minimum difference, D. The criteria returns
            true when the norm of the difference between xBest and xAtOptimal is less than
            or equal to D.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x.</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.ToKnownBestXConvergence.minDifference">
            <summary>
            Gets or sets the min difference.
            </summary>
            <value>The min difference.</value>
        </member>
        <member name="P:OptimizationToolbox.ToKnownBestXConvergence.xAtOptimal">
            <summary>
            Gets or sets the optimal x which the process should stop after reaching.
            </summary>
            <value>The x at optimal.</value>
        </member>
        <member name="F:OptimizationToolbox.PNormProportionalSelection.v">
            <summary>
              v is the power that q is raised to convert it to p.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.GetEnumerator">
            <summary>
            Returns an enumerator that iterates through the collection.
            </summary>
            <returns>
            A <see cref="T:System.Collections.Generic.IEnumerator`1"/> that can be used to iterate through the collection.
            </returns>
            <filterpriority>1</filterpriority>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.System#Collections#IEnumerable#GetEnumerator">
            <summary>
            Returns an enumerator that iterates through a collection.
            </summary>
            <returns>
            An <see cref="T:System.Collections.IEnumerator"/> object that can be used to iterate through the collection.
            </returns>
            <filterpriority>2</filterpriority>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.ContainsKey(System.Double[])">
            <summary>
            Determines whether the <see cref="T:System.Collections.Generic.IDictionary`2"/> contains an element with the specified key.
            </summary>
            <returns>
            true if the <see cref="T:System.Collections.Generic.IDictionary`2"/> contains an element with the key; otherwise, false.
            </returns>
            <param name="key">The key to locate in the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
                            </param><exception cref="T:System.ArgumentNullException"><paramref name="key"/> is null.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.Add(System.Double[],System.Double)">
            <summary>
            Adds an element with the provided key and value to the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </summary>
            <param name="key">The object to use as the key of the element to add.
                            </param><param name="value">The object to use as the value of the element to add.
                            </param><exception cref="T:System.ArgumentNullException"><paramref name="key"/> is null.
                            </exception><exception cref="T:System.ArgumentException">An element with the same key already exists in the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
                            </exception><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.IDictionary`2"/> is read-only.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.Remove(System.Double[])">
            <summary>
            Removes the element with the specified key from the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </summary>
            <returns>
            true if the element is successfully removed; otherwise, false.  This method also returns false if <paramref name="key"/> was not found in the original <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </returns>
            <param name="key">The key of the element to remove.
                            </param><exception cref="T:System.ArgumentNullException"><paramref name="key"/> is null.
                            </exception><exception cref="T:System.NotSupportedException">The <see cref="T:System.Collections.Generic.IDictionary`2"/> is read-only.
                            </exception>
        </member>
        <member name="M:OptimizationToolbox.optFunctionData.TryGetValue(System.Double[],System.Double@)">
            <summary>
            Gets the value associated with the specified key.
            </summary>
            <returns>
            true if the object that implements <see cref="T:System.Collections.Generic.IDictionary`2"/> contains an element with the specified key; otherwise, false.
            </returns>
            <param name="key">The key whose value to get.
                            </param><param name="value">When this method returns, the value associated with the specified key, if the key is found; otherwise, the default value for the type of the <paramref name="value"/> parameter. This parameter is passed uninitialized.
                            </param><exception cref="T:System.ArgumentNullException"><paramref name="key"/> is null.
                            </exception>
        </member>
        <member name="P:OptimizationToolbox.optFunctionData.Item(System.Double[])">
            <summary>
            Gets or sets the element with the specified key.
            </summary>
            <returns>
            The element with the specified key.
            </returns>
            <param name="key">The key of the element to get or set.
                            </param><exception cref="T:System.ArgumentNullException"><paramref name="key"/> is null.
                            </exception><exception cref="T:System.Collections.Generic.KeyNotFoundException">The property is retrieved and <paramref name="key"/> is not found.
                            </exception><exception cref="T:System.NotSupportedException">The property is set and the <see cref="T:System.Collections.Generic.IDictionary`2"/> is read-only.
                            </exception>
        </member>
        <member name="P:OptimizationToolbox.optFunctionData.Keys">
            <summary>
            Gets an <see cref="T:System.Collections.Generic.ICollection`1"/> containing the keys of the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </summary>
            <returns>
            An <see cref="T:System.Collections.Generic.ICollection`1"/> containing the keys of the object that implements <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </returns>
        </member>
        <member name="P:OptimizationToolbox.optFunctionData.Values">
            <summary>
            Gets an <see cref="T:System.Collections.Generic.ICollection`1"/> containing the values in the <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </summary>
            <returns>
            An <see cref="T:System.Collections.Generic.ICollection`1"/> containing the values in the object that implements <see cref="T:System.Collections.Generic.IDictionary`2"/>.
            </returns>
        </member>
        <member name="M:OptimizationToolbox.equalToValue.GetSchema">
            <summary>
            This method is reserved and should not be used. When implementing the IXmlSerializable interface, you should return null (Nothing in Visual Basic)
             from this method, and instead, if specifying a custom schema is required, apply the <see cref="T:System.Xml.Serialization.XmlSchemaProviderAttribute"/> to the class.
            </summary>
            <returns>
            An <see cref="T:System.Xml.Schema.XmlSchema"/> that describes the XML representation of the object that is produced by the <see cref="M:System.Xml.Serialization.IXmlSerializable.WriteXml(System.Xml.XmlWriter)"/> method and consumed by the <see cref="M:System.Xml.Serialization.IXmlSerializable.ReadXml(System.Xml.XmlReader)"/> method.
            </returns>
        </member>
        <member name="M:OptimizationToolbox.equalToValue.ReadXml(System.Xml.XmlReader)">
            <summary>
            Generates an object from its XML representation.
            </summary>
            <param name="reader">The <see cref="T:System.Xml.XmlReader"/> stream from which the object is deserialized. 
                            </param>
        </member>
        <member name="M:OptimizationToolbox.equalToValue.WriteXml(System.Xml.XmlWriter)">
            <summary>
            Converts an object into its XML representation.
            </summary>
            <param name="writer">The <see cref="T:System.Xml.XmlWriter"/> stream to which the object is serialized. 
                            </param>
        </member>
        <member name="P:OptimizationToolbox.HillClimbing.neighborGenerator">
            <summary>
            Gets the neighbor generator.
            </summary>
            <value>The neighbor generator.</value>
        </member>
        <member name="P:OptimizationToolbox.HillClimbing.selector">
            <summary>
            Gets the selector.
            </summary>
            <value>The selector.</value>
        </member>
        <member name="M:OptimizationToolbox.CuboidParetoSelector.dominates(OptimizationToolbox.Candidate,OptimizationToolbox.Candidate)">
            <summary>
            Does c1 dominate c2?
            </summary>
            <param name="c1">the subject candidate, c1 (does this dominate...).</param>
            <param name="c2">the object candidate, c2 (is dominated by).</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.VariableDescriptorEnum.Dispose">
            <summary>
            Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
            </summary>
            <filterpriority>2</filterpriority>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestFConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ToKnownBestFConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestFConvergence.#ctor(System.Double,System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ToKnownBestFConvergence"/> class.
            </summary>
            <param name="fAtOptimal">The f at optimal.</param>
            <param name="positiveTolerance">The positive tolerance.</param>
            <param name="negativeTolerance">The negative tolerance.</param>
        </member>
        <member name="M:OptimizationToolbox.ToKnownBestFConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value for fAtOptimal and a tolerance on either side. The criteria returns
            true when the value of fBest is within the range of fAtOptimal +/- tolerance. One
            is welcome to set the tolerance to 0. The default of infinity is fine if it is on
            the opposite side of the optimization direction.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f.</param>
            <param name="xBest">The best x (not used).</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.ToKnownBestFConvergence.fAtOptimal">
            <summary>
            Gets or sets the optimal f which the process should stop after reaching.
            </summary>
            <value>The f at optimal.</value>
        </member>
        <member name="P:OptimizationToolbox.ToKnownBestFConvergence.positiveTolerance">
            <summary>
            Gets or sets the tolerance negative on the positive side of fAtOptimal.
            </summary>
            <value>The positive tolerance.</value>
        </member>
        <member name="P:OptimizationToolbox.ToKnownBestFConvergence.negativeTolerance">
            <summary>
            Gets or sets the tolerance negative on the negative side of fAtOptimal.
            It should be stored as a positive number, but either way the absolute value
            is taken to make the range about fAtOptimal.
            </summary>
            <value>The negative tolerance.</value>
        </member>
        <member name="T:OptimizationToolbox.MaxIterationsConvergence">
            <summary>
            Given a value Kmax, this criteria will return true if the process reaches this many iterations.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxIterationsConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxIterationsConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxIterationsConvergence.#ctor(System.Int64)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxIterationsConvergence"/> class.
            </summary>
            <param name="maxIterations">The max iterations.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxIterationsConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value Kmax, this criteria will return true if the process reaches this many iterations.
            </summary>
            <param name="iteration">The number of iterations.</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x (not used).</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.MaxIterationsConvergence.maxIterations">
            <summary>
            Gets or sets the maximum number of iterations.
            </summary>
            <value>The max iterations.</value>
        </member>
        <member name="T:OptimizationToolbox.MaxTimeConvergence">
            <summary>
            
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxTimeConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxTimeConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxTimeConvergence.#ctor(System.TimeSpan)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxTimeConvergence"/> class.
            </summary>
            <param name="maxTime">The max time.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxTimeConvergence.#ctor(System.DateTime)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxTimeConvergence"/> class.
            </summary>
            <param name="timeToStop">The time to stop.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxTimeConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a value for maxTime, this criteria will return true, when the process reaches
            this length of time. It does not use any of the arguments below, but in some ways
            is the most user-friendly criteria. Use wisely. Use often.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations.</param>
            <param name="fBest">The best f.</param>
            <param name="xBest">The best x.</param>
            <param name="population">The population of candidates.</param>
            <param name="gradF">The gradient of F.</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.MaxTimeConvergence.maxTime">
            <summary>
            Gets or sets the maximum time span for the process.
            </summary>
            <value>The max time.</value>
        </member>
        <member name="P:OptimizationToolbox.VariableDescriptor.Discrete">
            <summary>
              Gets or sets a value indicating whether this <see cref="T:OptimizationToolbox.VariableDescriptor"/> is discrete.
            </summary>
            <value><c>true</c> if discrete; otherwise, <c>false</c>.</value>
        </member>
        <member name="M:OptimizationToolbox.MaxAgeConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxAgeConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxAgeConvergence.#ctor(System.Int32,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxAgeConvergence"/> class.
            </summary>
            <param name="maxAge">The max age.</param>
            <param name="toleranceForSame">The tolerance for same.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxAgeConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Internally keeps track of the age of the best candidate. If it exceeds the given MaxAge value, 
            the criteria will return true. This is to say, if no better candidate is found in MaxAge 
            iterations, return true.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x.</param>
            <param name="population">The population of candidates (not used).</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.MaxAgeConvergence.toleranceForSame">
            <summary>
            Gets or sets the tolerance for same candidate (used to increment the internal age of best).
            </summary>
            <value>The tolerance for same.</value>
        </member>
        <member name="P:OptimizationToolbox.MaxAgeConvergence.maxAge">
            <summary>
            Gets or sets the max age.
            </summary>
            <value>The max age.</value>
        </member>
        <member name="T:OptimizationToolbox.ProblemDefinition">
            <summary>
            This class is used only for the XML serialization and deserialization of 
            optimization problems. The saving and opening of problems allows one to 
            compare the performance across multiple techniques without have to re-
            write a bunch of code. Note that types of elements stored in this file.
            The representation of the problem is captured by the SpaceDescriptor:
               how many variables and what are there ranges (if specified) and which
               are discrete, and which are continuous. Additionally, a starting
               point, xStart, can also be provided s.t. the optimization methods
               start at the same location.
            The evaluation of the problem is essentially described by the f's, g's and h's
               functions (note that these employ a special class simply for proper
               XML serialization, ListforIOptFunctions).
            Convergence methods are really a part of the optimization method, but these
               can stored within the problem definition so that one can compare different
               methods operating under the same convergence criteria.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ProblemDefinition.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ProblemDefinition"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ProblemDefinition.Add(System.Object)">
            <summary>
            Adds the specified object to the problem definition as is similiarly done 
            for abstractOptMethod.
            </summary>
            <param name="function">The function.</param>
        </member>
        <member name="M:OptimizationToolbox.ProblemDefinition.saveProbToXml(System.String)">
            <summary>
            Saves the problem definition to XML.
            </summary>
            <param name="filename">The filename.</param>
        </member>
        <member name="M:OptimizationToolbox.ProblemDefinition.openprobFromXml(System.String)">
            <summary>
            Open the problem definition from XML.
            </summary>
            <param name="filename">The filename.</param>
            <returns></returns>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.name">
            <summary>
            Gets or sets the name.
            </summary>
            <value>The name.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.xStart">
            <summary>
            Gets or sets the x start.
            </summary>
            <value>The x start.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.ConvergenceMethods">
            <summary>
            Gets or sets the convergence methods.
            </summary>
            <value>The convergence methods.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.NumConvergeCriteriaNeeded">
            <summary>
            Gets or sets the num convergence criteria needed to stop the process.
            </summary>
            <value>The num converge criteria needed.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.SpaceDescriptor">
            <summary>
            Gets or sets the space descriptor.
            </summary>
            <value>The space descriptor.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.f">
            <summary>
            Gets or sets the f.
            </summary>
            <value>The f.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.g">
            <summary>
            Gets or sets the g.
            </summary>
            <value>The g.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.h">
            <summary>
            Gets or sets the h.
            </summary>
            <value>The h.</value>
        </member>
        <member name="P:OptimizationToolbox.ProblemDefinition.FunctionList">
            <summary>
            Gets or sets the function list.
            </summary>
            <value>The function list.</value>
        </member>
        <member name="T:OptimizationToolbox.ListforIOptFunctions">
            <summary>
            This little class is a necessary evil once I removed the abstract classes. The reason being that
            interfaces are not (directly) serializable as abstract classes are. The problem definition
            class avoids any "discussion" of interfaces so that we can easily use the XmlSerializer
            automatically. However, for the list of functions (objective functions, inequalities and equalities)
            we use this, which overrides (thanks to the IXmlSerializable Members) how the lists are 
            serialized.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ListforIOptFunctions.#ctor(System.Collections.Generic.List{OptimizationToolbox.IObjectiveFunction},System.Collections.Generic.List{OptimizationToolbox.IInequality},System.Collections.Generic.List{OptimizationToolbox.IEquality})">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.ListforIOptFunctions"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.ListforIOptFunctions.GetSchema">
            <summary>
              This method is reserved and should not be used. When implementing the IXmlSerializable interface, you should return null (Nothing in Visual Basic) from this method, and instead, if specifying a custom schema is required, apply the <see cref = "T:System.Xml.Serialization.XmlSchemaProviderAttribute" /> to the class.
            </summary>
            <returns>
              An <see cref = "T:System.Xml.Schema.XmlSchema" /> that describes the XML representation of the object that is produced by the <see cref = "M:System.Xml.Serialization.IXmlSerializable.WriteXml(System.Xml.XmlWriter)" /> method and consumed by the <see cref = "M:System.Xml.Serialization.IXmlSerializable.ReadXml(System.Xml.XmlReader)" /> method.
            </returns>
        </member>
        <member name="T:OptimizationToolbox.SequentialQuadraticProgramming">
            <summary>
            </summary>
        </member>
        <member name="M:OptimizationToolbox.SequentialQuadraticProgramming.formulateActiveSetAndGradients(System.Double[])">
            <summary>
              Formulates the active set and gradients.
            </summary>
            <param name = "xk">The xk.</param>
            <returns>the gradient of the active constraints as an m by n matrix.</returns>
        </member>
        <member name="M:OptimizationToolbox.SequentialQuadraticProgramming.calculateSQPSearchDirection(System.Double[],System.Double@)">
            <summary>
              Calculates the SQP search direction.
            </summary>
            <param name = "xk">The xk.</param>
            <param name = "initAlpha">The init alpha.</param>
            <returns></returns>
        </member>
        <member name="M:OptimizationToolbox.GoldenSection.#ctor(System.Double,System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.GoldenSection"/> class.
            Unlike other line search methods, there is no need to provide a kmax.
            This is determined directly from the epsilon in the body of the code
            </summary>
            <param name="epsilon">The epsilon.</param>
            <param name="upperBound">The upper bound.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxSpanInPopulationConvergence.#ctor">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxSpanInPopulationConvergence"/> class.
            </summary>
        </member>
        <member name="M:OptimizationToolbox.MaxSpanInPopulationConvergence.#ctor(System.Double)">
            <summary>
            Initializes a new instance of the <see cref="T:OptimizationToolbox.MaxSpanInPopulationConvergence"/> class.
            </summary>
            <param name="MinimumSpan">The minimum span.</param>
        </member>
        <member name="M:OptimizationToolbox.MaxSpanInPopulationConvergence.converged(System.Int64,System.Int64,System.Double,System.Collections.Generic.IList{System.Double},System.Collections.Generic.IList{System.Double[]},System.Collections.Generic.IList{System.Double})">
            <summary>
            Given a minimum span, S, this criteria returns true when the span is equal to 
            or less than S. This is probably the slowest criteria (p*log(p)) given that it must
            check the distance between every pair of solutions in the population. But, probably 
            not an significant increase  for p less than 1000.
            </summary>
            <param name="iteration">The number of iterations (not used).</param>
            <param name="numFnEvals">The number of function evaluations (not used).</param>
            <param name="fBest">The best f (not used).</param>
            <param name="xBest">The best x (not used).</param>
            <param name="population">The population of candidates.</param>
            <param name="gradF">The gradient of F (not used).</param>
            <returns>
            true or false - has the process converged?
            </returns>
        </member>
        <member name="P:OptimizationToolbox.MaxSpanInPopulationConvergence.MinimumSpan">
            <summary>
            Gets or sets the minimum span of the set of solutions found in the population.
            The span is the farthest euclidian distance between any two solutions.
            </summary>
            <value>The minimum span.</value>
        </member>
    </members>
</doc>
